The eco-evolutionary dynamics of two strategic species: From the predator-prey to the innocent-spreader rumor model.

Species frequently engage in both competitive and cooperative interactions, delicately balancing these dynamics to optimize their chances of survival and reproduction. While competition drives individuals to compete for limited resources, cooperation can emerge as a strategic response, mitigating risk and enhancing collective payoff. To bridge theoretical game approaches such as payoff, cooperation, and defections in ecological systems, we propose a two-species predator-prey model inspired by the principles and variations of the prisoner's dilemma game. We comprehensively address and analytically verify all stable strategic states, exploring the role of payoff parameters both individually and collectively. Additionally, we investigate the effect of free space. Beyond ecological contexts, we present a model of rumor propagation within a social system to establish connections with the prisoner's dilemma game. In both systems, our primary focus is to discuss strategies and enhance the cooperative factor within the system, given its crucial importance across diverse environments.

Microbial markets: socio-economic perspective in studying microbial communities.

Studying microbial communities through a socio-economic lens, this paper draws parallels with human economic transactions and microbes' race for resources. Extending the 'Market Economy' concept of social science to microbial ecosystems, the paper aims to contribute to comprehending the collaborative and competitive dynamics among microorganisms. Created by a multidisciplinary team of an economist, microbiologists, and mathematicians, the paper also highlights the risks involved in employing a socio-economic perspective to explain the complexities of natural ecosystems. Navigating through microbial markets offers insights into the implications of these interactions while emphasizing the need for cautious interpretation within the broader ecological context. We hope that this paper will be a fruitful source of inspiration for future studies on microbial communities.

A need for systems thinking and the appliance of (complexity) science in healthcare.

Hospitals represent complex adaptive systems where interactions and relationships of different components both affect and shape the way they work simultaneously. Pressures on hospitals determine how they behave and many of the problems seen in the NHS and indeed other health services can be viewed through the lens of complexity science and systems thinking. 'Flow' of patients through the hospital can be seen as an indicator of how well the hospital 'system' is working. The better flow is, the more patients can be treated and the less time is spent waiting in the various queues that accrue around the hospital, In this article, we explore the impact of these disciplines on patient flow and examine how short-term and overly simple solutions can exacerbate problems in the health service, despite the best intentions of those working in it. Many of today's problems can be described in terms of 'system archetypes' and 'game theory'. Understanding this may lead to improvement in how services are redesigned to solve these problems.

Convergence of reputations under indirect reciprocity.

Previous research has shown how indirect reciprocity can promote cooperation through evolutionary game theoretic models. Most work in this field assumes a separation of time-scales: individuals' reputations equilibrate at a fast time scale for given frequencies of strategies while the strategies change slowly according to the replicator dynamics. Much of the previous research has focused on the behaviour and stability of equilibria for the replicator dynamics. Here we focus on the underlying reputational dynamics that occur on a fast time scale. We describe reputational dynamics as systems of differential equations and conduct stability analyses on their equilibria. We prove that reputations converge to a unique equilibrium under a solitary observer model for each of the five standard norms and whether assessments are public or private. These results confirm a crucial but previously understudied assumption underlying the theory of indirect reciprocity for the most studied set of norms.

Emergence of cooperation in the one-shot Prisoner's dilemma through Discriminatory and Samaritan AIs.

As artificial intelligence (AI) systems are increasingly embedded in our lives, their presence leads to interactions that shape our behaviour, decision-making and social interactions. Existing theoretical research on the emergence and stability of cooperation, particularly in the context of social dilemmas, has primarily focused on human-to-human interactions, overlooking the unique dynamics triggered by the presence of AI. Resorting to methods from evolutionary game theory, we study how different forms of AI can influence cooperation in a population of human-like agents playing the one-shot Prisoner's dilemma game. We found that Samaritan AI agents who help everyone unconditionally, including defectors, can promote higher levels of cooperation in humans than Discriminatory AI that only helps those considered worthy/cooperative, especially in slow-moving societies where change based on payoff difference is moderate (small intensities of selection). Only in fast-moving societies (high intensities of selection), Discriminatory AIs promote higher levels of cooperation than Samaritan AIs. Furthermore, when it is possible to identify whether a co-player is a human or an AI, we found that cooperation is enhanced when human-like agents disregard AI performance. Our findings provide novel insights into the design and implementation of context-dependent AI systems for addressing social dilemmas.

CSR strategies seasonal cycling: A new mechanism for coexistence among seaweeds.

The stable maintenance of high biological diversity remains a major puzzle in biology. We propose a new mechanism involving the cyclical use of Competitive, Stress-tolerant, and Ruderal (CSR) strategies to explain high biodiversity maintenance. This study examines the interactions among three morphs of the cosmopolitan and commercially important seaweed Ulva Linnaeus. We measured biomass productivity, effective quantum yield, carbohydrate concentration, and nutrient competition across all seasons for one year and matched trait value combinations to CSR strategies. Our findings reveal that the Ulva morphs exhibited significant competitive interactions under eutrophic conditions, in a scramble competition dynamic. However, competition did not significantly affect their functional traits under naturally prevalent oligotrophic conditions. Season-by-season analysis revealed that each morph employed temporal niche partitioning by cyclically adopting different CSR strategies, thereby avoiding direct competition. This cyclical strategy, akin to a rock-paper-scissors game, prevents any single strategy from dominating year-round, maintaining the three-morph polymorphism. Our study further highlights the importance of year-long functional trait measurements to encompass seasonal changes in functional responses. Our CSR cycling conceptual model offers new insights useful for monitoring and conservation efforts.

Comparative evaluation of different recycling modes for decommissioned photovoltaic products based on stackelberg game.

China is the photovoltaic (PV) leader worldwide and will be confronted with significant challenges stemming from the scrap tide of PV products. Constructing an effective recycling system is essential for retired PV product management. Using the Stackelberg game theory, this study establishes and compares three recycling modes including manufacturer recycling, third-party recycling, and producer responsibility organization (PRO) recycling for decommissioned PV products. Afterward, the effects of module processing costs, echelon utilization rates, and collection subsidies on the transfer prices, collection quantities, supply chain profits, and carbon emissions of the various recycling modes are simulated and analyzed. The results reveal that: (1) The manufacturer recycling realizes optimal supply chain profits; (2) Compared to the PRO recycling mode, the third-party recycling experiences superior performances when retired module processing costs are lower than a specific threshold; (3) Uplifting echelon utilization rates and collection subsidies while reducing module processing costs could supplement the overall economic and environmental benefits within the PV closed-loop supply chain (CLSC); (4) Environmental performances of the different recycling modes are associated with the carbon emission reduction efficiency. Accordingly, valuable insights are provided for manufacturers, recyclers, and governments to develop a sustainable retired PV product recycling system.

Agent-based evolutionary game dynamics uncover the dual role of resource heterogeneity in the evolution of cooperation.

Cooperation is a cornerstone of social harmony and group success. Environmental feedbacks that provide information about resource availability play a crucial role in encouraging cooperation. Previous work indicates that the impact of resource heterogeneity on cooperation depends on the incentive to act in self-interest presented by a situation, demonstrating its potential to both hinder and facilitate cooperation. However, little is known about the underlying evolutionary drivers behind this phenomenon. Leveraging agent-based modeling and game theory, we explore how differences in resource availability across environments influence the evolution of cooperation. Our results show that resource variation hinders cooperation when resources are slowly replenished but supports cooperation when resources are more readily available. Furthermore, simulations in different scenarios suggest that discerning the rate of natural selection acts on strategies under distinct evolutionary dynamics is instrumental in elucidating the intricate nexus between resource variability and cooperation. When evolutionary forces are strong, resource heterogeneity tends to work against cooperation, yet relaxed selection conditions enable it to facilitate cooperation. Inspired by these findings, we also propose a potential application in improving the performance of artificial intelligence systems through policy optimization in multi-agent reinforcement learning. These explorations promise a novel perspective in understanding the evolution of social organisms and the impact of different interactions on the function of natural systems.

Parrondo's paradox reveals counterintuitive wins in biology and decision making in society.

Parrondo's paradox refers to the paradoxical phenomenon of combining two losing strategies in a certain manner to obtain a winning outcome. It has been applied to uncover unexpected outcomes across various disciplines, particularly at different spatiotemporal scales within ecosystems. In this article, we provide a comprehensive review of recent developments in Parrondo's paradox within the interdisciplinary realm of the physics of life, focusing on its significant applications across biology and the broader life sciences. Specifically, we examine its relevance from genetic pathways and phenotypic regulation, to intercellular interaction within multicellular organisms, and finally to the competition between populations and species in ecosystems. This phenomenon, spanning multiple biological domains and scales, enhances our understanding of the unified characteristics of life and reveals that adaptability in a drastically changing environment, rather than the inherent excellence of a trait, underpins survival in the process of evolution. We conclude by summarizing our findings and discussing future research directions that hold promise for advancing the field.

Assessing and managing the suicidal patient: forget the Reverend Bayes and try game theory.

Probability-based estimates of the future suicide of psychiatric patients are of little assistance in clinical practice. This article proposes strategic management of the interaction between the clinician and the patient in the assessment of potentially suicidal patients, using principles derived from game theory, to achieve a therapeutic outcome that minimises the likelihood of suicide. Further developments in the applications of large language models could allow us to quantify the basis for clinical decisions in individual patients. Documenting the basis of those decisions would help to demonstrate an adequate standard of care in every interaction.